docstr: ivy.Array instance method variant of ivy.random_normal. This method
  simply wraps the function, and so the docstring for ivy.random_normal
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      The mean of the normal distribution to sample from. Default is ``0.0``.
  std
      The standard deviation of the normal distribution to sample from.
      Must be non-negative. Default is ``1.0``.
  shape
      If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
      are drawn. Can only be specified when ``mean`` and ``std`` are numeric
      values, else exception will be raised.
      Default is ``None``, where a single value is returned.
  device
      device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
      (Default value = None).
  dtype
       output array data type. If ``dtype`` is ``None``, the output array data
       type will be the default floating-point data type. Default ``None``
  seed
      A python integer. Used to create a random seed distribution
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Drawn samples from the parameterized normal distribution.
  
  Examples
  --------
  >>> x = ivy.array([[9.8, 3.4], [5.8, 7.2]])
  >>> x.random_normal(std=10.2)
  ivy.array([[19.   , -6.44 ],
             [ 5.72 ,  0.235]])
  
  >>> x.random_normal(std=10.2, device='cpu')
  ivy.array([[18.7 , 25.2 ],
             [27.5 , -3.22]])
  
  >>> x.random_normal(std=14.2, dtype='float16')
  ivy.array([[26.6 , 12.1 ],
             [ 4.56,  5.49]])
  
  >>> x.random_normal(std=10.8, device='cpu', dtype='float64')
  ivy.array([[ 1.02, -1.39],
             [14.2 , -1.  ]])
  
  >>> z = ivy.ones((2,2))
  >>> x.random_normal(std=11.2, device='cpu', dtype='float64', out=z)
  ivy.array([[ 7.72, -8.32],
             [ 4.95, 15.8 ]])
  
  >>> x = ivy.array([8.7, 9.3])
  >>> y = ivy.array([12.8, 14.5])
  >>> x.random_normal(std=y)
  ivy.array([-10.8,  12.1])
  
  >>> x.random_normal(std=y, device='cpu')
  ivy.array([ 13. , -26.9])
  
  >>> x.random_normal(std=y, dtype='float16')
  ivy.array([14.3  , -0.807])
  
  >>> x.random_normal(std=y, device='cpu', dtype='float64')
  ivy.array([21.3 ,  3.85])
  
  >>> z = ivy.ones((2,))
  >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
  ivy.array([ 4.32, 42.2 ])
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code: def random_normal(
      self: ivy.Array,
      /,
      *,
      std: Union[float, ivy.Array, ivy.NativeArray] = 1.0,
      shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      device: Optional[Union[ivy.Device, ivy.NativeDevice]] = None,
      dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype]] = None,
      seed: Optional[int] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.random_normal. This method
      simply wraps the function, and so the docstring for ivy.random_normal
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          The mean of the normal distribution to sample from. Default is ``0.0``.
      std
          The standard deviation of the normal distribution to sample from.
          Must be non-negative. Default is ``1.0``.
      shape
          If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
          are drawn. Can only be specified when ``mean`` and ``std`` are numeric
          values, else exception will be raised.
          Default is ``None``, where a single value is returned.
      device
          device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
          (Default value = None).
      dtype
           output array data type. If ``dtype`` is ``None``, the output array data
           type will be the default floating-point data type. Default ``None``
      seed
          A python integer. Used to create a random seed distribution
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Drawn samples from the parameterized normal distribution.
  
      Examples
      --------
      >>> x = ivy.array([[9.8, 3.4], [5.8, 7.2]])
      >>> x.random_normal(std=10.2)
      ivy.array([[19.   , -6.44 ],
                 [ 5.72 ,  0.235]])
  
      >>> x.random_normal(std=10.2, device='cpu')
      ivy.array([[18.7 , 25.2 ],
                 [27.5 , -3.22]])
  
      >>> x.random_normal(std=14.2, dtype='float16')
      ivy.array([[26.6 , 12.1 ],
                 [ 4.56,  5.49]])
  
      >>> x.random_normal(std=10.8, device='cpu', dtype='float64')
      ivy.array([[ 1.02, -1.39],
                 [14.2 , -1.  ]])
  
      >>> z = ivy.ones((2,2))
      >>> x.random_normal(std=11.2, device='cpu', dtype='float64', out=z)
      ivy.array([[ 7.72, -8.32],
                 [ 4.95, 15.8 ]])
  
      >>> x = ivy.array([8.7, 9.3])
      >>> y = ivy.array([12.8, 14.5])
      >>> x.random_normal(std=y)
      ivy.array([-10.8,  12.1])
  
      >>> x.random_normal(std=y, device='cpu')
      ivy.array([ 13. , -26.9])
  
      >>> x.random_normal(std=y, dtype='float16')
      ivy.array([14.3  , -0.807])
  
      >>> x.random_normal(std=y, device='cpu', dtype='float64')
      ivy.array([21.3 ,  3.85])
  
      >>> z = ivy.ones((2,))
      >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
      ivy.array([ 4.32, 42.2 ])
      """
      return ivy.random_normal(
          mean=self._data,
          std=std,
          shape=shape,
          device=device,
          dtype=dtype,
          seed=seed,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/random.py
imports: import abc
  from typing import Optional, Union
  import ivy
label_desc: Use image node_id 2 for calling the _ArrayWithRandom obj's underlying member method code with example usage: obj.random_normal() and returns: ivy